AI-200 Data Management Services And Vector Search Practice Test 7 — 15 Questions

Question 1

Which Azure AI Search capability allows you to extract text from images and PDF documents during indexer execution using AI cognitive skills?

Accepted Answer

Skillsets. Skillsets in Azure AI Search define a pipeline of cognitive skills (such as OCR, image analysis, entity recognition, and text translation) that run during indexer execution to enrich content. To extract text from images and PDFs, you attach a skillset to the indexer, and skills like OCR or Document Extraction process the content. Semantic configurations, scoring profiles, and vector profiles do not perform content extraction.

Answer

Semantic configurations

Answer

Scoring profiles

Answer

Vector profiles

Question 2

You are writing a Python script to query an Azure AI Search index using the azure-search-documents SDK. You want to retrieve both vector matches and keyword matches using hybrid search. Which class should you instantiate to represent the vector query?

Accepted Answer

VectorizedQuery. In the azure-search-documents Python SDK, a vector query is represented by the VectorizedQuery class, which holds the embedding vector, the k-nearest-neighbors count, and the fields to search. You pass a list of VectorizedQuery objects to the search client's search method alongside a keyword query string to perform hybrid search. This class is the SDK's canonical representation of a vector query payload.

Answer

EmbeddingQueryClient

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HnswSearchRequest

Answer

CosmosVectorSearch

Question 3

You are designing a high-availability RAG architecture where search queries must succeed even if an entire Azure region experiences an outage. You deploy Azure AI Search in a primary region and a secondary region. How should client applications handle failover?

Accepted Answer

Use Azure Traffic Manager or Azure Front Door in front of regional search service endpoints with health probes. Azure Traffic Manager and Azure Front Door are the correct global load-balancing services to place in front of regional Azure AI Search endpoints, using health probes to detect regional outages and route client traffic to a healthy secondary region. Traffic Manager operates at the DNS layer with priority or performance routing, while Front Door provides anycast-based Layer 7 routing with faster failover and additional WAF/TLS offload. Both support health probe configuration that automatically removes an unhealthy regional endpoint from rotation.

Answer

Hardcode the secondary search IP address in client application configuration files

Answer

Rely on Cosmos DB automatic failover to replicate search indices

Answer

Configure SQL replication between search indexes

Question 4

You are configuring an Azure AI Search index where text fields are automatically analyzed using language-specific rules. Which property on an Edm.String field specifies the language analyzer (e.g., 'en.microsoft')?

Accepted Answer

analyzer. In Azure AI Search, the 'analyzer' property on an Edm.String field specifies the language analyzer (e.g., 'en.microsoft', 'fr.lucene') that governs tokenization, stemming, and stop-word removal for that field. Setting it per-field enables language-specific text processing so queries match documents correctly across languages.

Answer

tokenizerType

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languageMapping

Answer

vectorProfile

Question 5

You are designing a secure data layer architecture for financial documents using Azure Cosmos DB for NoSQL and Azure AI Search. Which THREE compliance and security controls should you implement? (Choose three)

Accepted Answer

Enable diagnostic logging and audit log streaming to Azure Monitor / Log Analytics. Option B is correct because enabling diagnostic logging and streaming audit logs to Azure Monitor / Log Analytics provides the tamper-evident activity trail and monitoring required for regulatory compliance (e.g., PCI DSS, SOX) on financial document workloads. Option D is correct because configuring Customer-Managed Keys (CMK) via Azure Key Vault gives the organization control over the encryption keys protecting data at rest in Cosmos DB, satisfying requirements for key ownership, rotation, and revocation that Microsoft-managed keys do not meet. Option E is correct because restricting network access with Virtual Network service endpoints and Azure Private Link removes public internet exposure, ensuring only approved private endpoints within the VNet can reach the Cosmos DB and Azure AI Search resources. Option A is wrong because storing backups in unencrypted public blob containers directly violates encryption-at-rest and access-control mandates. Option C is wrong because disabling authentication keys and allowing anonymous public access eliminates authentication and authorization, which is unacceptable for sensitive financial data.

Answer

Store all database backup files in unencrypted public blob storage containers

Answer

Disable authentication keys and allow anonymous public access for easy integration

Question 6

Which Azure service provides cloud-scale vector search backed by either HNSW or DiskANN algorithms alongside managed NoSQL capabilities?

Accepted Answer

Azure Cosmos DB for NoSQL. Azure Cosmos DB for NoSQL natively supports vector search with HNSW and DiskANN algorithms integrated into its managed, globally distributed NoSQL database. It combines the vector index with the document store, so embeddings and metadata live in the same container and are queried with SQL-like syntax.

Answer

Azure Logic Apps

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Azure Functions

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Azure Virtual Machines

Question 7

You are troubleshooting a performance issue where an Azure AI Search indexer fails to complete within the scheduled execution window when processing large PDF documents. What is the most effective configuration change to resolve the timeout issue?

Accepted Answer

Configure the indexer maxRunTime property and scale out search units or adjust batch size. Indexer timeouts on large PDFs are typically caused by the indexer exceeding the scheduled run window while skillset enrichment and document cracking proceed slowly. Increasing maxRunTime extends the allowed execution window, and scaling out search units (or reducing batch size) increases throughput so the indexer completes within the schedule. These are the documented levers for resolving indexer timeout issues in Azure AI Search.

Answer

Convert all vector fields to Edm.String

Answer

Switch the search service to the Free tier

Answer

Delete all semantic configurations from the index

Question 8

When configuring vector search in Azure AI Search, which TWO parameters must be defined when creating a vector profile? (Choose two)

Accepted Answer

algorithm. When creating a vector profile in Azure AI Search, the two required parameters are the profile's name and the algorithm configuration it references. Option B (name) is correct because every vector profile must have a unique name within the search index so it can be referenced by vector fields via the vectorSearchProfile property. Option A (algorithm) is correct because a vector profile must point to a configured vector search algorithm (for example, an HNSW or exhaustive KNN configuration defined in the index's algorithms collection) that determines how the vector index is built and queried. Options C (sqlConnectionString), D (vmSize), and E (cosmosDatabaseName) are incorrect because they are unrelated to Azure AI Search vector profile definitions: sqlConnectionString and cosmosDatabaseName pertain to data source connections for indexers, and vmSize relates to compute sizing in other Azure services, not to vector profile configuration.

Answer

sqlConnectionString

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vmSize

Answer

cosmosDatabaseName

Question 9

You are designing a high-performance RAG data layer architecture using Azure AI Search. Which THREE indexing and retrieval strategies should you implement to maximize recall and precision? (Choose three)

Accepted Answer

Enable semantic ranker to re-order top results using deep learning models. Option A is correct because Azure AI Search's semantic ranker applies deep-learning language models (Microsoft's semantic re-ranking) to re-order the top BM25/vector results, improving precision by promoting semantically relevant documents that lexical or vector scoring alone may rank lower. Option C is correct because HNSW (Hierarchical Navigable Small World) is the approximate nearest neighbor algorithm used in Azure AI Search vector indexes, and tuning m (number of bi-directional links per node), efConstruction (index-time candidate list size), and efSearch (query-time candidate list size) directly controls the recall-versus-latency tradeoff — higher values increase recall at the cost of latency. Option E is correct because hybrid search fuses BM25 keyword scoring with vector similarity (via Reciprocal Rank Fusion in Azure AI Search), capturing both exact term matches and semantic similarity, which raises recall and precision versus either method alone. Option B is incorrect because disabling scoring and ranking would eliminate relevance ordering entirely, destroying precision rather than maximizing it. Option D is incorrect because embeddings must be stored in vector fields (Collection(Edm.Single) with a vector profile and HNSW configuration) to be searchable; storing them as plain text in unindexed fields makes them unusable for vector retrieval.

Answer

Disable all scoring and ranking algorithms to save CPU cycles

Answer

Store embeddings as plain text strings in unindexed fields

Question 10

Which Azure AI Search field property indicates whether the contents of a field can be returned in search query result documents?

Accepted Answer

retrievable. In Azure AI Search, the 'retrievable' attribute controls whether a field's contents are returned in search results. If retrievable is false, the field can still be searched, filtered, or sorted but its value is omitted from the returned document. This is the exact property that governs result payload inclusion.

Answer

searchable

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facetable

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filterable

Question 11

You are designing an AI architecture that stores vector embeddings in Azure Cosmos DB for NoSQL. You need to implement role-based access control (RBAC) so that specific application microservices can only execute read-only queries against the database without having permission to write or delete documents. How should you configure this?

Accepted Answer

Create a custom SQL role definition with read-only data actions and assign it to the microservice's managed identity. Azure Cosmos DB for NoSQL has a native RBAC system built on SQL role definitions and role assignments. A custom role definition can be scoped to specific data-plane actions such as 'Microsoft.DocumentDB/databaseAccounts/readMetadata' and 'Microsoft.DocumentDB/databaseAccounts/sqlDatabases/containers/items/read', granting read-only access. Assigning that role to the microservice's managed identity enforces least privilege without exposing keys.

Answer

Configure Azure Blob Storage RBAC permissions on the Cosmos DB container

Answer

Share the primary master master-key with all microservices

Answer

Disable authentication on the Cosmos DB account during query execution

Question 12

When configuring vector search in Azure AI Search, which TWO components are required to successfully define and execute a vector search query? (Choose two)

Accepted Answer

A vector query payload specifying the query vector and target field. Option D is correct because Azure AI Search requires the index schema to include a vector field (created with the Collection(Edm.Single) data type and a dimensions value) before any vector search can be performed against it; without a defined vector field there is nothing to query. Option C is correct because executing a vector search requires a query payload that supplies the query vector (for example, "vectorQueries" with "kind": "vector", "vector": [...], and "fields": "<vectorFieldName>") along with the target vector field, which the search engine uses to compute similarity. Options A, B, and E are incorrect because a Cosmos DB partition key, an Azure SQL connection string, and an HDInsight Hadoop endpoint are unrelated to Azure AI Search vector query definition and execution; vector search relies on the search index schema and query payload, not on those external data-store or big-data components.

Answer

A Cosmos DB container partition key

Answer

An Azure SQL Server database connection string

Answer

An HDInsight Hadoop cluster endpoint

Question 13

You are designing a disaster recovery and backup strategy for Azure Cosmos DB for NoSQL storing vector embeddings. Which THREE backup options or capabilities are available? (Choose three)

Accepted Answer

Restoring backed-up data to a new Azure Cosmos DB account. Option A is correct because Azure Cosmos DB restore operations always create a new account (or restore into a new account) rather than overwriting the existing account, which is the standard mechanism for both periodic and continuous backup restores. Option C is correct because the periodic backup mode supports configurable backup intervals and retention periods (with defaults of every 4 hours and 2 copies retained, extendable via support), letting you tailor the schedule to your RPO needs. Option D is correct because continuous backup mode enables point-in-time restore (PITR) for the last 30 days (or 7 days in some configurations), which is ideal for protecting vector embedding data against accidental deletes or corruption. Option B is not applicable because Cosmos DB for NoSQL is not SQL Server and does not use .bak files or native SQL Server restore tooling. Option E is not a supported capability because Cosmos DB does not provide FTP-based manual file export into local VM storage as a backup mechanism.

Answer

Direct SQL Server .bak file restoration

Answer

Manual file export via FTP into local VM storage

Question 14

You are building an AI search solution using Azure AI Search. You want to ensure that users searching for acronyms or synonyms receive relevant results even if the exact keyword does not appear in the document. Which feature should you configure?

Accepted Answer

Synonym maps. Synonym maps in Azure AI Search let you define equivalence rules so that a query term expands to its synonyms or acronym expansions at query time. This ensures documents containing the full term are returned even when the user searches with an acronym, and vice versa. Synonym maps are applied per-field and are the correct feature for handling acronyms and synonyms.

Answer

Scoring profiles

Answer

Semantic ranker

Answer

Vector profiles

Question 15

When configuring vector search in Azure Cosmos DB for NoSQL, which TWO steps are required to prepare a container for vector search before inserting documents? (Choose two)

Accepted Answer

Define the vectorEmbeddingPolicy at container creation or update. Option A is correct because Azure Cosmos DB for NoSQL requires a vectorEmbeddingPolicy on the container to declare the vector properties, including the path to the vector field, the data type (e.g., float32), dimensions, and distance function (cosine, dotproduct, or euclidean) before vectors can be stored and queried. Option B is correct because you must also define vectorIndexes inside the container's indexingPolicy, specifying the vector path and index type (flat, quantizedFlat, or diskANN), so the vector data is actually indexed and searchable. Options C, D, and E are incorrect: NFS shares, the pgvector extension, and relational foreign keys belong to other systems (file storage, PostgreSQL, and relational databases respectively) and are not part of Cosmos DB for NoSQL vector search configuration.

Answer

Mount an NFS network share for vector file storage

Answer

Install the pgvector extension on the container

Answer

Create a relational foreign key constraint to Azure SQL